Identifying critical states of hepatocellular carcinoma based on landscape dynamic network biomarkers

2020 ◽  
Vol 85 ◽  
pp. 107202 ◽  
Author(s):  
Yichen Sun ◽  
Hongqian Zhao ◽  
Min Wu ◽  
Junhua Xu ◽  
Shanshan Zhu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Dynamic Network ◽  
Critical States ◽  
Network Biomarkers ◽  
Landscape Dynamic

scholarly journals Quantifying critical states of complex diseases using single-sample dynamic network biomarkers

2017 ◽  
Vol 13 (7) ◽  
pp. e1005633 ◽  
Author(s):  
Xiaoping Liu ◽  
Xiao Chang ◽  
Rui Liu ◽  
Xiangtian Yu ◽  
Luonan Chen ◽  
...  
Keyword(s):  
Dynamic Network ◽  
Complex Diseases ◽  
Single Sample ◽  
Critical States ◽  
Network Biomarkers

Identifying critical states of hepatocellular carcinoma based on single-sample dynamic network biomarkers combined with simulated annealing algorithm

2021 ◽  
Vol 16 ◽  
Author(s):  
Hongqian Zhao ◽  
Jie Gao ◽  
Yichen Sun ◽  
Yujie Wang ◽  
Tianhao Guan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Simulated Annealing ◽  
Critical State ◽  
Simulated Annealing Algorithm ◽  
Dynamic Network ◽  
Disease State ◽  
Statistical Characteristics ◽  
Low Grade ◽  
Network Biomarkers ◽  
Annealing Algorithm

Background: Hepatocellular carcinoma(HCC) is one of the most common malignant tumors. Due to the insidious onset and poor prognosis, most patients have reached the advanced stage at the time of diagnosis. Objective: Studies have shown thatdynamic network biomarkers (DNB) can effectively identify the critical state of complex diseases such as HCC from normal state to disease state. Therefore, it is very important to detect DNB efficiently and reliably. Methods: This paper selects a dataset containing eight HCC disease states. First, anindividual-specific network is constructed for each sample and features are extracted. In the context of this network, a simulated annealing algorithm is used to search for potential dynamic network biomarker modules, and the evolution of HCC is determined. Results: In fact, in the period of low-grade dysplasia (LGD) and high-grade dysplasia (HGD), DNB will send an indicative warning signal, which means that liver dysplasia is a very important critical state in the development of HCC disease. Compared with landscape dynamic network biomarkers method (L-DNB), our method can not only describe the statistical characteristics of each disease state, but also yield better results including getting more DNBs enriched in HCC related pathways. Conclusion: The results of this study may be of great significance to the prevention and early diagnosis of HCC.

scholarly journals Detection for disease tipping points by landscape dynamic network biomarkers

2018 ◽  
Vol 6 (4) ◽  
pp. 775-785 ◽  
Author(s):  
Xiaoping Liu ◽  
Xiao Chang ◽  
Siyang Leng ◽  
Hui Tang ◽  
Kazuyuki Aihara ◽  
...  
Keyword(s):  
Dynamic Network ◽  
Good Prognosis ◽  
Single Sample ◽  
Physiological Data ◽  
Tipping Points ◽  
Virus Infections ◽  
Model Free ◽  
Network Biomarkers ◽  
Landscape Dynamic ◽  
The Individual

ABSTRACT A new model-free method has been developed and termed the landscape dynamic network biomarker (l-DNB) methodology. The method is based on bifurcation theory, which can identify tipping points prior to serious disease deterioration using only single-sample omics data. Here, we show that l-DNB provides early-warning signals of disease deterioration on a single-sample basis and also detects critical genes or network biomarkers (i.e. DNB members) that promote the transition from normal to disease states. As a case study, l-DNB was used to predict severe influenza symptoms prior to the actual symptomatic appearance in influenza virus infections. The l-DNB approach was then also applied to three tumor disease datasets from the TCGA and was used to detect critical stages prior to tumor deterioration using an individual DNB for each patient. The individual DNBs were further used as individual biomarkers in the analysis of physiological data, which led to the identification of two biomarker types that were surprisingly effective in predicting the prognosis of tumors. The biomarkers can be considered as common biomarkers for cancer, wherein one indicates a poor prognosis and the other indicates a good prognosis.

scholarly journals Personalized Early-Warning Signals during Progression of Human Coronary Atherosclerosis by Landscape Dynamic Network Biomarker

Genes ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 676
Author(s):  
Jing Ge ◽  
Chenxi Song ◽  
Chengming Zhang ◽  
Xiaoping Liu ◽  
Jingzhou Chen ◽  
...  
Keyword(s):  
Early Warning ◽  
Coronary Atherosclerosis ◽  
Dynamic Network ◽  
Tipping Point ◽  
Warning Signals ◽  
Metabolomics Data ◽  
Early Warning Signals ◽  
Network Biomarkers ◽  
Landscape Dynamic ◽  
Major Factors

Coronary atherosclerosis is one of the major factors causing cardiovascular diseases. However, identifying the tipping point (predisease state of disease) and detecting early-warning signals of human coronary atherosclerosis for individual patients are still great challenges. The landscape dynamic network biomarkers (l-DNB) methodology is based on the theory of dynamic network biomarkers (DNBs), and can use only one-sample omics data to identify the tipping point of complex diseases, such as coronary atherosclerosis. Based on the l-DNB methodology, by using the metabolomics data of plasma of patients with coronary atherosclerosis at different stages, we accurately detected the early-warning signals of each patient. Moreover, we also discovered a group of dynamic network biomarkers (DNBs) which play key roles in driving the progression of the disease. Our study provides a new insight into the individualized early diagnosis of coronary atherosclerosis and may contribute to the development of personalized medicine.

scholarly journals Identifying Dysregulated lncRNA-Associated ceRNA Network Biomarkers in CML Based on Dynamical Network Biomarkers

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Junhua Xu ◽  
Min Wu ◽  
Yichen Sun ◽  
Hongqian Zhao ◽  
Yujie Wang ◽  
...  
Keyword(s):  
Myeloid Leukemia ◽  
Clinical Medicine ◽  
Critical Index ◽  
Dynamic Network ◽  
Disease Outbreaks ◽  
Chronic Phase ◽  
Cerna Network ◽  
Network Biomarkers ◽  
Timely Treatment ◽  
Competitive Endogenous Rna

The incidence of chronic myeloid leukemia (CML) is increasing year by year, which is a serious threat to human health. Early diagnosis can reduce mortality and improve prognosis. LncRNAs have been shown to be effective biomarkers for a variety of diseases and can act as competitive endogenous RNA (ceRNA). In this study, the dysregulated lncRNA-associated ceRNA networks (DLCN) of the chronic phase (CP), accelerated phase (AP), and blastic crisis (BC) for CML are constructed. Then, based on dynamic network biomarkers (DNB), some dysregulated lncRNA-associated ceRNA network biomarkers of CP, AP, and BC for CML are identified according to DNB criteria. Thus, a lncRNA (SNHG5) is identified from DLCN_CP, a lncRNA (DLEU2) is identified from DLCN_AP, and two lncRNAs (SNHG3, SNHG5) are identified from DLCN_BC. In addition, the critical index (CI) used to detect disease outbreaks shows that CML occurs in CP, which is consistent with clinical medicine. By analyzing the functions of the identified ceRNA network biomarkers, it has been found that they are effective lncRNA biomarkers directly or indirectly related to CML. The result of this study will help deepen the understanding of CML pathology from the perspective of ceRNA and help discover the effective biomarkers of CP, AP, and BC for CML in the future, so as to help patients get timely treatment and reduce the mortality of CML.

SMAD7 and SERPINE1 as novel dynamic network biomarkers detect and regulate the tipping point of TGF-beta induced EMT

2020 ◽  
Vol 65 (10) ◽  
pp. 842-853
Author(s):  
Zhonglin Jiang ◽  
Lina Lu ◽  
Yuwei Liu ◽  
Si Zhang ◽  
Shuxian Li ◽  
...  
Keyword(s):  
Dynamic Network ◽  
Tipping Point ◽  
Tgf Beta ◽  
Network Biomarkers

scholarly journals Identifying pre-outbreak signals of hand, foot and mouth disease based on landscape dynamic network marker

2021 ◽  
Vol 21 (S1) ◽  
Author(s):  
Xuhang Zhang ◽  
Rong Xie ◽  
Zhengrong Liu ◽  
Yucong Pan ◽  
Rui Liu ◽  
...  
Keyword(s):  
Seasonal Pattern ◽  
Social Systems ◽  
Dynamic Network ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Control Measures ◽  
Foot And Mouth ◽  
Set Up ◽  
Landscape Dynamic ◽  
Clinic Visits

Abstract Background The high incidence, seasonal pattern and frequent outbreaks of hand, foot and mouth disease (HFMD) represent a threat for billions of children around the world. Detecting pre-outbreak signals of HFMD facilitates the timely implementation of appropriate control measures. However, real-time prediction of HFMD outbreaks is usually challenging because of its complexity intertwining both biological systems and social systems. Results By mining the dynamical information from city networks and horizontal high-dimensional data, we developed the landscape dynamic network marker (L-DNM) method to detect pre-outbreak signals prior to the catastrophic transition into HFMD outbreaks. In addition, we set up multi-level early warnings to achieve the purpose of distinguishing the outbreak scale. Specifically, we collected the historical information of clinic visits caused by HFMD infection between years 2009 and 2018 respectively from public records of Tokyo, Hokkaido, and Osaka, Japan. When applied to the city networks we modelled, our method successfully identified pre-outbreak signals in an average 5 weeks ahead of the HFMD outbreak. Moreover, from the performance comparisons with other methods, it is seen that the L-DNM based system performs better when given only the records of clinic visits. Conclusions The study on the dynamical changes of clinic visits in local district networks reveals the dynamic or landscapes of HFMD spread at the network level. Moreover, the results of this study can be used as quantitative references for disease control during the HFMD outbreak seasons.

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